crs-l2-svalbard-polar-areas
Map (reference ← swipe → agent)
0:00
svalbard_glaciers — I need an honest size ranking of these high-latitude glaciers using true geographic area, not the distorted values you'd get from raw lat/lon coordinates. Pick the most appropriate coordinate system for measuring area at these latitudes and give me the top 20 by area as CSV: columns name, area_km2, bbox_minx_polar, bbox_miny_polar, bbox_maxx_polar, bbox_maxy_polar, crs_epsg — bboxes in the same projected metres as the area, and crs_epsg the integer EPSG code of the CRS you used. Sort by area_km2 descending. File: svalbard_glaciers_top20.csv.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:12
Let me start by exploring what data we have about Svalbard glaciers.
0:12
Bash
ls /work/
svalbard_glaciers_wgs84.gpkg
0:18
Let's first examine the data to understand its structure and what CRS it's in.
0:18
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('svalbard_glaciers_wgs84.gpkg')
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print(gdf.head(3))
"
Columns: ['id', 'name', 'subtype', 'class', 'geometry']
CRS: EPSG:4326
Shape: (169, 5)
id ... geometry
0 af2fac62-8f17-35dd-bb4b-cd7ac5f906b0 ... MULTIPOLYGON (((22.78865 80.14451, 22.73235 80...
1 12ba70b6-6f49-35fa-a072-bfdc8321c987 ... MULTIPOLYGON (((14.02517 77.97816, 14.02557 77...
2 5919887f-cdb2-3283-a884-b1146bdda8ee ... MULTIPOLYGON (((16.06338 78.22499, 16.06381 78...
[3 rows x 5 columns]
0:27
Let me think about the most appropriate coordinate system for measuring area at Svalbard's latitudes (roughly 74°N to 81°N). For polar regions, the best choices are: 1. **Polar Stereographic** projections - specifically EPSG:3995 (WGS 84 / Arctic Polar Stereographic) or EPSG:3413 (NSIDC Sea Ice Polar Stereographic North) 2. **UTM zones** - Svalbard spans multiple UTM zones (31-37), so that's not ideal for the whole area 3. **Lambert Azimuthal Equal-Area** - EPSG:3571 (North Pole, LAEA) or EPSG:3575 (WGS 84 / LAEA North Pole) The most appropriate for Svalbard for area measurement would be a polar stereographic projection. EPSG:3995 (WGS 84 / Arctic Polar Stereographic) is a standard choice. Let also consider EPSG:3413 which is also commonly used. Actually, for measuring area accurately in the Arctic, EPSG:3995 (Arctic Polar Stereographic) is widely used. But let me also check EPSG:3575 (WGS 84 / LAEA North Pole). Let me check the bounding box to get the extent.
0:27
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('svalbard_glaciers_wgs84.gpkg')
print('Total glaciers:', len(gdf))
print('Total bounds:', gdf.total_bounds)
# Check name availability
print('Names sample:', gdf['name'].value_counts().head(10))
print('Null names:', gdf['name'].isna().sum())
"
Total glaciers: 169 Total bounds: [10.7877123 77.4630249 33.4866675 80.3115547] Names sample: name Ahlmannfonna 1 Aldegondabreen 1 Altbreen 1 Austgötabreen 1 Austre Brøggerbreen 1 Austre Grønfjordbreen 1 Austre Helvetiabreen 1 AustreLovenbreen 1 Baalsrudbreen 1 Bertilbreen 1 Name: count, dtype: int64 Null names: 0
0:52
For Svalbard (latitudes ~77°N to ~81°N), the most appropriate projected CRS for measuring area accurately would be a Lambert Azimuthal Equal Area projection. The EPSG code 6931 is the NSIDC EASE-Grid 2.0 North (EPSG:6931) which is a Lambert Azimuthal Equal Area projection centered on the North Pole. Another option is EPSG:3413 (NSIDC Sea Ice Polar Stereographic North). Actually, for Svalbard specifically, let's think about this more carefully. The best choice for area measurement at high latitudes is an equal-area projection. Common options: 1. EPSG:6931 - EASE-Grid 2.0 North (Lambert Azimuthal Equal Area, center at North Pole) 2. EPSG:3413 - NSIDC Sea Ice Polar Stereographic North 3. EPSG:3995 - Arctic Polar Stereographic 4. EPSG:3575 - LAEA North Pole (Lambert Azimuthal Equal Area) Let me compute areas using a few different CRS to compare. Actually, EPSG:6931 (EASE-Grid 2.0 North) is a good choice because it's a Lambert Azimuthal Equal Area projection specifically designed for the Northern Hemisphere. The center of EPSG:6931 is at 0°N, 0°E... wait no. Let me check. EPSG:6931 - EASE-Grid 2.0 North, Lambert Azimuthal Equal Area, center at 90°N, 0°E (North Pole). This is designed for equal-area measurements in the Arctic. EPSG:3413 - WGS 84 NSIDC Sea Ice Polar Stereographic North, center at 90°N, 0°E. Also commonly used for Arctic. Let me use EPSG:6931 (EASE-Grid 2.0 North) as it's explicitly Lambert Azimuthal Equal Area which preserves area. Or EPSG:3571? Let me just go with EPSG:6931. Actually, let me compute areas with both to see and pick one. Let me write a script to do all the work.
0:52
Write
/work/solve.py
content · 45 lines · py
import geopandas as gpd
import pandas as pd
# Read the data
gdf = gpd.read_file('/work/svalbard_glaciers_wgs84.gpkg')
print(f"Total glaciers: {len(gdf)}")
print(f"Original CRS: {gdf.crs}")
# Svalbard spans ~77°N to ~80°N. Best equal-area CRS for the Arctic:
# EPSG:6931 — EASE-Grid 2.0 North (Lambert Azimuthal Equal Area, centred at North Pole)
# EPSG:3413 — NSIDC Sea Ice Polar Stereographic North
#
# Let's test both and pick the one that gives most stable results.
target_crs = 'EPSG:6931'
# Project to the polar equal-area CRS
gdf_proj = gdf.to_crs(target_crs)
print(f"Projected CRS: {gdf_proj.crs}")
# Compute area in km²
gdf_proj['area_km2'] = gdf_proj.geometry.area / 1_000_000
# Compute bounding box in projected coordinates
gdf_proj['bbox_minx_polar'] = gdf_proj.geometry.bounds['minx']
gdf_proj['bbox_miny_polar'] = gdf_proj.geometry.bounds['miny']
gdf_proj['bbox_maxx_polar'] = gdf_proj.geometry.bounds['maxx']
gdf_proj['bbox_maxy_polar'] = gdf_proj.geometry.bounds['maxy']
# Build result dataframe
result = gdf_proj[['name', 'area_km2', 'bbox_minx_polar', 'bbox_miny_polar',
'bbox_maxx_polar', 'bbox_maxy_polar']].copy()
# Add CRS column
result['crs_epsg'] = 6931
# Sort by area descending, take top 20
result = result.sort_values('area_km2', ascending=False).head(20).reset_index(drop=True)
print("\nTop 20 glaciers:")
print(result.to_string())
# Write CSV
result.to_csv('/work/svalbard_glaciers_top20.csv', index=False)
print("\nWritten to /work/svalbard_glaciers_top20.csv")Wrote /work/solve.py (1522 chars).
0:53
Let me run this script to see if it works.
0:53
Bash
cd /work && python solve.py
Total glaciers: 169
Original CRS: EPSG:4326
Projected CRS: EPSG:6931
Top 20 glaciers:
name area_km2 bbox_minx_polar bbox_miny_polar bbox_maxx_polar bbox_maxy_polar crs_epsg
0 Sørfonna 4335.832923 408721.813591 -1.107462e+06 511711.867601 -1.036927e+06 6931
1 Vestfonna 2378.883369 357790.574708 -1.081987e+06 423181.181074 -1.016737e+06 6931
2 Åsgardfonna 911.981123 314043.056692 -1.141665e+06 353268.419032 -1.087734e+06 6931
3 Kvitøyjøkulen 623.623478 574342.073878 -9.452687e+05 604942.479003 -9.067828e+05 6931
4 Valhallfonna 423.704226 331830.622702 -1.109960e+06 355442.622432 -1.077761e+06 6931
5 Nordmannsfonna 130.748908 405284.649934 -1.249107e+06 423647.163338 -1.231056e+06 6931
6 Sveabreen 126.287900 289455.662292 -1.239005e+06 315298.274620 -1.219149e+06 6931
7 Gruvfonna 113.630721 375736.392354 -1.292146e+06 394989.378792 -1.274945e+06 6931
8 Veteranen 96.499807 349095.402100 -1.156268e+06 361603.088084 -1.131113e+06 6931
9 Himingbjørgfonna 84.108312 336458.715681 -1.152460e+06 351734.656784 -1.138206e+06 6931
10 Hellefonna 82.139947 394719.048336 -1.269626e+06 405163.316388 -1.252096e+06 6931
11 Heuglinbreen 71.062312 409287.747056 -1.229658e+06 420771.435638 -1.216976e+06 6931
12 Nordenskiöldbreen 66.923677 367639.290114 -1.211711e+06 378079.923079 -1.198117e+06 6931
13 Eidembreen 66.894492 288405.246393 -1.263001e+06 303436.362361 -1.250251e+06 6931
14 Isrosa 46.949925 411587.559798 -1.254317e+06 423957.967683 -1.246300e+06 6931
15 Edvardbreen 46.232427 402432.715666 -1.295299e+06 413325.287887 -1.280938e+06 6931
16 Esmarkbreen 42.860919 303651.266873 -1.266793e+06 313962.332139 -1.255978e+06 6931
17 Venernbreen 42.524210 294349.027681 -1.267239e+06 306668.981174 -1.257587e+06 6931
18 Dunérbreen 40.495260 325773.741955 -1.096691e+06 334302.651087 -1.083442e+06 6931
19 Fridtjovbreen 39.080140 331355.818749 -1.318962e+06 341964.068761 -1.307916e+06 6931
Written to /work/svalbard_glaciers_top20.csv
1:00
Let me verify the CSV output and also consider if there's a better CRS choice. Let me think about this. For measuring area at high latitudes (Svalbard: 77°N to 80°N), the best choices are: 1. **EPSG:6931** - EASE-Grid 2.0 North (Lambert Azimuthal Equal Area) - This is a global equal-area projection for the Northern Hemisphere, centred at the North Pole. Good for area measurement. 2. **EPSG:3413** - NSIDC Sea Ice Polar Stereographic North - Standard for Arctic sea ice but might have more distortion further from the pole. 3. **EPSG:3995** - Arctic Polar Stereographic - uses 71°N standard parallel Actually, for Svalbard at ~78-80°N, the Lambert Azimuthal Equal Area (EPSG:6931) is a great choice because it's truly equal-area. But let me double-check by looking at the projected coordinates — the y values are negative which is expected for a polar projection (centered at the pole, so south of the pole = negative y). Wait, actually EPSG:6931 is a Global EASE-Grid 2.0 projection for the Northern Hemisphere. Its center is at the North Pole. The coordinates make sense - negative y values for areas south of the North Pole. Let me verify the CSV output.
1:00
Read
/work/svalbard_glaciers_top20.csv
1 name,area_km2,bbox_minx_polar,bbox_miny_polar,bbox_maxx_polar,bbox_maxy_polar,crs_epsg
2 Sørfonna,4335.832922856602,408721.81359061133,-1107462.309931236,511711.86760089203,-1036927.3085099282,6931
3 Vestfonna,2378.8833688893474,357790.5747075411,-1081986.6049921028,423181.181074032,-1016737.0805504997,6931
4 Åsgardfonna,911.9811226005821,314043.0566921155,-1141665.3745437327,353268.41903234227,-1087733.578962999,6931
5 Kvitøyjøkulen,623.6234779132017,574342.0738783373,-945268.701455067,604942.479002696,-906782.7839848535,6931
6 Valhallfonna,423.70422581483245,331830.6227019347,-1109959.9771559115,355442.6224322258,-1077761.321506332,6931
7 Nordmannsfonna,130.74890764203852,405284.6499335777,-1249107.4477856834,423647.16333769006,-1231056.3006689083,6931
8 Sveabreen,126.2879001405774,289455.66229236504,-1239004.9606821379,315298.27461951517,-1219149.46131264,6931
9 Gruvfonna,113.63072091859425,375736.39235363214,-1292145.9631017558,394989.37879219995,-1274945.1292681177,6931
10 Veteranen,96.49980724192062,349095.4020995356,-1156267.911419928,361603.08808434417,-1131112.5810777992,6931
11 Himingbjørgfonna,84.10831215427905,336458.715680702,-1152460.3162962834,351734.6567844486,-1138205.992968631,6931
12 Hellefonna,82.13994711525382,394719.04833617085,-1269626.1134295817,405163.31638784904,-1252096.2356806868,6931
13 Heuglinbreen,71.06231153338486,409287.74705644517,-1229657.5498550078,420771.4356380981,-1216975.5492626964,6931
14 Nordenskiöldbreen,66.92367694614374,367639.29011383734,-1211710.6555203549,378079.9230790797,-1198116.6594402054,6931
15 Eidembreen,66.89449174473046,288405.24639306415,-1263000.8091489668,303436.3623612909,-1250251.2437658939,6931
16 Isrosa,46.94992547451299,411587.55979823,-1254317.4240829898,423957.9676829317,-1246299.6685940067,6931
17 Edvardbreen,46.232427338592025,402432.7156661522,-1295299.4997031784,413325.2878865115,-1280937.9442175026,6931
18 Esmarkbreen,42.860919192101555,303651.2668730174,-1266793.1269868494,313962.3321393784,-1255977.5414705607,6931
19 Venernbreen,42.52421033614556,294349.0276805889,-1267238.8811511798,306668.98117359047,-1257587.3924585679,6931
20 Dunérbreen,40.49525952148226,325773.7419547589,-1096690.67506587,334302.65108744864,-1083441.7002606895,6931
21 Fridtjovbreen,39.080139509413016,331355.8187490857,-1318962.2782728462,341964.0687609846,-1307916.473975751,6931
1:02
The CSV looks good. Let me verify it's well-formed and clean.
1:02
1:02
Bash
cd /work && python -c "
cd /work && python -c "
import pandas as pd
df = pd.read_csv('svalbard_glaciers_top20.csv')
print('Shape:', df.shape)
print('Columns:', df.columns.tolist())
print('CRS EPSG:', df['crs_epsg'].iloc[0])
print('Sorted descending:', df['area_km2'].is_monotonic_decreasing)
print(df.to_string())
"
Shape: (20, 7)
Columns: ['name', 'area_km2', 'bbox_minx_polar', 'bbox_miny_polar', 'bbox_maxx_polar', 'bbox_maxy_polar', 'crs_epsg']
CRS EPSG: 6931
Sorted descending: True
name area_km2 bbox_minx_polar bbox_miny_polar bbox_maxx_polar bbox_maxy_polar crs_epsg
0 Sørfonna 4335.832923 408721.813591 -1.107462e+06 511711.867601 -1.036927e+06 6931
1 Vestfonna 2378.883369 357790.574708 -1.081987e+06 423181.181074 -1.016737e+06 6931
2 Åsgardfonna 911.981123 314043.056692 -1.141665e+06 353268.419032 -1.087734e+06 6931
3 Kvitøyjøkulen 623.623478 574342.073878 -9.452687e+05 604942.479003 -9.067828e+05 6931
4 Valhallfonna 423.704226 331830.622702 -1.109960e+06 355442.622432 -1.077761e+06 6931
5 Nordmannsfonna 130.748908 405284.649934 -1.249107e+06 423647.163338 -1.231056e+06 6931
6 Sveabreen 126.287900 289455.662292 -1.239005e+06 315298.274620 -1.219149e+06 6931
7 Gruvfonna 113.630721 375736.392354 -1.292146e+06 394989.378792 -1.274945e+06 6931
8 Veteranen 96.499807 349095.402100 -1.156268e+06 361603.088084 -1.131113e+06 6931
9 Himingbjørgfonna 84.108312 336458.715681 -1.152460e+06 351734.656784 -1.138206e+06 6931
10 Hellefonna 82.139947 394719.048336 -1.269626e+06 405163.316388 -1.252096e+06 6931
11 Heuglinbreen 71.062312 409287.747056 -1.229658e+06 420771.435638 -1.216976e+06 6931
12 Nordenskiöldbreen 66.923677 367639.290114 -1.211711e+06 378079.923079 -1.198117e+06 6931
13 Eidembreen 66.894492 288405.246393 -1.263001e+06 303436.362361 -1.250251e+06 6931
14 Isrosa 46.949925 411587.559798 -1.254317e+06 423957.967683 -1.246300e+06 6931
15 Edvardbreen 46.232427 402432.715666 -1.295299e+06 413325.287887 -1.280938e+06 6931
16 Esmarkbreen 42.860919 303651.266873 -1.266793e+06 313962.332139 -1.255978e+06 6931
17 Venernbreen 42.524210 294349.027681 -1.267239e+06 306668.981174 -1.257587e+06 6931
18 Dunérbreen 40.495260 325773.741955 -1.096691e+06 334302.651087 -1.083442e+06 6931
19 Fridtjovbreen 39.080140 331355.818749 -1.318962e+06 341964.068761 -1.307916e+06 6931
1:08
Everything looks good. Let me provide a summary to the user.
1:08